private Image FindBitmap(String fileName)
{
String rootName = OSservices.GetRootName(fileName);
String fullFileName = OSservices.LookForFile(rootName + ".bmp", rootDir);
if (String.IsNullOrEmpty(fullFileName))
{
return(null);
}
else
{
return(new Bitmap(fullFileName));
}
}
} /* LoadNextImageFromDir */
private String LocateRootName(String rootName)
{
String subDir = null;
if (!String.IsNullOrEmpty(dir))
{
subDir = OSservices.LookForFile(rootName + ".bmp", dir);
}
if (String.IsNullOrEmpty(subDir))
{
if (!String.IsNullOrEmpty(dir2))
{
subDir = OSservices.LookForFile(rootName + ".bmp", dir2);
if (subDir == null)
{
return(null);
}
}
}
return(OSservices.AddSlash(subDir) + rootName + ".bmp");
} /* LocateRootName */
} /* LoadImageGrid */
private void UpdateDetailProbabilityDisplay(String fileName)
{
if (fileName == null)
{
return;
}
curSelImageFileName = fileName;
String fullFileName = null;
if ((fileName.Length > 1) && (fileName[1] == ':'))
{
fullFileName = fileName;
}
else
{
fullFileName = OSservices.AddSlash(rootDir) + fileName;
}
if (!File.Exists(fullFileName))
{
String fn = OSservices.GetRootName(fileName) + ".bmp";
fullFileName = OSservices.LookForFile(fn, rootDir);
}
Prediction prediction = GetPrediction(fileName);
// We need to call 'PredictProbabilities' twice. Once to get the intermediate images and the
// second time to perform prediction using the FeatureVector that was used in the original
// prediction. It has to be done this way because the version of 'PredictProbabilities' that
// derives the FeatureVector from an image does not always have access of instrumentation data.
PicesRasterList featureCalcImages = new PicesRasterList();
PicesPredictionList guesses2 = prediction.trainModel.PredictProbabilities(fullFileName, featureCalcImages);
// Perform Prediction using original FeatureVector.
PicesPredictionList guesses = prediction.trainModel.PredictProbabilities(prediction.fv);
ImageFileName.Text = fileName;
if (guesses == null)
{
return;
}
ClassificationProbabilities.Rows.Clear();
foreach (PicesPrediction p in guesses)
{
Object[] data = new Object[3];
data[0] = p.ClassName;
data[1] = p.Probability;
data[2] = p.Votes;
ClassificationProbabilities.Rows.Add(data);
}
FeatureCalcImages.Rows.Clear();
if ((featureCalcImages == null) || (featureCalcImages.Count < 1))
{
return;
}
for (int x = 0; x < featureCalcImages.Count; x++)
{
Object[] data = new Object[2];
data[0] = featureCalcImages[x].BuildBitmap();
data[1] = featureCalcImages[x].FileName;
FeatureCalcImages.Rows.Add(data);
}
} /* UpdateDetailProbabilityDisplay */
